Physiological parameter system

ABSTRACT

A physiological parameter system has one or more parameter inputs responsive to one or more physiological sensors. The physiological parameter system may also have quality indicators relating to confidence in the parameter inputs. A processor is adapted to combine the parameter inputs, quality indicators and predetermined limits for the parameters inputs and quality indicators so as to generate alarm outputs or control outputs or both.

PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS

The present application claims priority benefit under 35 U.S.C. §119(e)of U.S. Provisional Application Ser. No. 60/876,749, filed Dec. 22,2006, entitled “Physiological Parameter System,” which is incorporatedherein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to a sensor for measuring physiologicalparameters and, in particular, relates to using measured physiologicalparameters to generate an indicator.

BACKGROUND

Pulse oximetry is a widely accepted noninvasive procedure for measuringthe oxygen saturation level of arterial blood, an indicator of aperson's oxygen supply. Early detection of a low blood oxygen level iscritical in the medical field, for example in critical care and surgicalapplications, because an insufficient supply of oxygen can result inbrain damage and death in a matter of minutes. A typical pulse oximetrysystem utilizes a sensor applied to a patient's finger. The sensor hasan emitter configured with both red and infrared LEDs that project lightthrough the finger to a detector so as to determine the ratio ofoxygenated and deoxygenated hemoglobin light absorption. In particular,the detector generates first and second intensity signals responsive tothe red and IR wavelengths emitted by the LEDs after absorption byconstituents of pulsatile blood flowing within a fleshy medium, such asa finger tip. A pulse oximetry sensor is described in U.S. Pat. No.6,088,607 titled Low Noise Optical Probe, which is assigned to MasimoCorporation, Irvine, Calif. and incorporated by reference herein.

Capnography comprises the continuous analysis and recording of carbondioxide concentrations in the respiratory gases of patients. The deviceused to measure the CO₂ concentrations is referred to as a capnometer.CO₂ monitoring can be performed on both intubated and non-intubatedpatients. With non-intubated patients, a nasal cannula is used.Capnography helps to identify situations that can lead to hypoxia ifuncorrected. Moreover, it also helps in the swift differential diagnosisof hypoxia before hypoxia can lead to irreversible brain damage. Pulseoximetry is a direct monitor of the oxygenation status of a patient.Capnography, on the other hand, is an indirect monitor that helps in thedifferential diagnosis of hypoxia so as to enable remedial measures tobe taken expeditiously before hypoxia results in an irreversible braindamage.

Early detection of low blood oxygen is critical in a wide variety ofmedical applications. For example, when a patient receives aninsufficient supply of oxygen in critical care and surgicalapplications, brain damage and death can result in just a matter ofminutes. Because of this danger, the medical industry developed pulseoximetry, a noninvasive procedure for measuring the oxygen saturation ofthe blood. A pulse oximeter interprets signals from a sensor attached toa patient in order to determine that patient's blood oxygen saturation.

A conventional pulse oximetry sensor has a red emitter, an infraredemitter, and a photodiode detector. The sensor is typically attached toa patient's finger, earlobe, or foot. For a finger, the sensor isconfigured so that the emitters project light from one side of thefinger, through the outer tissue of the finger, and into the bloodvessels and capillaries contained inside. The photodiode is positionedat the opposite side of the finger to detect the emitted light as itemerges from the outer tissues of the finger. The photodiode generates asignal based on the emitted light and relays that signal to the pulseoximeter. The pulse oximeter determines blood oxygen saturation bycomputing the differential absorption by the arterial blood of the twowavelengths (red and infrared) emitted by the sensor.

SUMMARY

Multiple physiological parameters, combined, provide a more powerfulpatient condition assessment tool than when any physiological parameteris used by itself. For example, a combination of parameters can providegreater confidence if an alarm condition is occurring. More importantly,such a combination can be used to give an early warning of a slowlydeteriorating patient condition as compared to any single parameterthreshold, which may not indicate such a condition for many minutes.Conditions such as hypovolemia, hypotension, and airway obstruction maydevelop slowly over time. A physiological parameter system that combinesmultiple parameters so as to provide an early warning could have a majoreffect on the morbidity and mortality outcome in such cases. Parameterscan include ECG, EKG, blood pressure, temperature, SpO₂, pulse rate,HbCO, HbMet, Hbt, SpaO2, HbO2, Hb, blood glucose, water, the presence orabsence of therapeutic drugs (aspirin, dapson, nitrates, or the like) orabusive drugs (methamphetamine, alcohol, or the like), concentrations ofcarbon dioxide (“CO2”), oxygen (“O”), ph levels, bilirubin, perfusionquality, signal quality, albumin, cyanmethemoglobin, and sulfhemoglobin(“HbSulf”) respiratory rate, inspiratory time, expiratory time,inspiratory to expiratory ratio, inspiratory flow, expiratory flow,tidal volume, minute volume, apnea duration, breath sounds—includingrales, rhonchi, or stridor, changes in breath sounds, heart rate, heartsounds—including S1, S2, S3, S4, or murmurs, or changes in heart sounds,or the like. Some references that have common shorthand designations arereferenced through such shorthand designations. For example, as usedherein, HbCO designates carboxyhemoglobin, HbMet designatesMethemoglobin, and Hbt designates total hemoglobin. Other shorthanddesignations such as COHb, MetHb, and tHb are also common in the art forthese same constituents. These constituents are generally reported interms of a percentage, often referred to as saturation, relativeconcentration or fractional saturation. Total hemoglobin is generallyreported as a concentration in g/dL. The use of the particular shorthanddesignators presented in this application does not restrict the term toany particular manner in which the designated constituent is reported.

Further, a greater emphasis has been put on decreasing the pain level ofpatients on the ward. Accordingly, patients are often given an IV setupthat enables the patient to increase the level of analgesia at will. Incertain situations, however, the patient's input must be ignored so asto avoid over medication. Complications from over sedation may includehypotension, tachycardia, bradycardia, hypoventilation and apnea. Aphysiological parameter system that uses pulse oximetry monitoring ofSpO₂ and pulse rate in conjunction with patient controlled analgesia(PCA) can aid in patient safety. Utilization of conventional pulseoximetry in conjunction with PCA, however, can result in the patientbeing erroneously denied pain medication. Conventional monitors aresusceptible to patient motion, which is likely to increase with risingpain. Further, conventional monitors do not provide an indication ofoutput reliability.

Advanced pulse oximetry is motion tolerant and also provides one or moreindications of signal quality or data confidence. These indicators canbe used as arbitrators in decision algorithms for adjusting the PCAadministration and sedation monitoring. Further, advanced pulse oximetrycan provide parameters in addition to oxygen saturation and pulse rate,such as perfusion index (PI). For example, hypotension can be assessedby changes in PI, which may be associated with changes in pulse rate.Motion tolerant pulse oximetry is described in U.S. Pat. No. 6,206,830titled Signal Processing Apparatus and Method; signal quality and dataconfidence indicators are described in U.S. Pat. No. 6,684,090 titledPulse Oximetry Data Confidence Indicator, both of which are assigned toMasimo Corporation, Irvine, Calif. and incorporated by reference herein.

One aspect of a physiological parameter system is a first parameterinput responsive to a first physiological sensor and a second parameterinput responsive to a second physiological sensor. A processor isadapted to combine the parameters and predetermined limits for theparameters so as to generate an indication of wellness.

Another aspect of a physiological parameter system is a parameter inputresponsive to a physiological sensor and a quality indicator inputrelating to confidence in the parameter input. A processor is adapted tocombine the parameter input, the quality indicator input andpredetermined limits for the parameter input and the quality indicatorinput so as to generate a control output.

A physiological parameter method comprises the steps of inputting aparameter responsive to a physiological sensor and inputting a qualityindicator related to data confidence for the parameter. A control signalis output from the combination of the parameter and the qualityindicator. The control signal is adapted to affect the operation of amedical-related device.

A method of improving the reporting of a physiological parameter in aphysiological parameter system comprises obtaining measurements of aphysiological parameter from a measurement site. At least some of thephysiological parameter measurements are maintained. A change in themeasurement site is detected. A measurement of the physiologicalparameter from a new measurement site is obtained. The measurement ofthe physiological parameter at the new measurement site is compared withthe maintained physiological parameter measurements. The magnitude ofthe physiological parameter reported by the physiological parametersystem at the new measurement site is adjusted to approximately matchthe magnitude of the maintained physiological parameter measurements.

A method of generating an indicator of patient wellness using aphysiological parameter system includes receiving physiologicalparameter data from a sensor attached to the physiological parametersystem. Physiological parameter preferences are provided to thephysiological parameter system. The physiological parameter data iscompared to the physiological parameter preferences. An indicator ofpatient wellness is generated by calculating a numerical wellness scorebased on the comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a physiological parametermeasurement system.

FIG. 2A illustrates an embodiment of a sensor assembly.

FIGS. 2B-C illustrate alternative sensor embodiments.

FIG. 3A illustrates an example chart of the value of a physiologicalparameter as measured by a sensor during a time when the sensor is movedfrom one measurement site to another.

FIG. 3B illustrates a chart of a physiological parameter reported by ameasurement system employing signal normalization techniques.

FIG. 3C illustrates a chart of a MetHb reading which is smoothed toaccount for abnormal variations in the readings.

FIG. 3D illustrates a MetHb smoothing flowchart.

FIG. 3E illustrates a system of multiple different MetHb calculatorswhich determine MetHb using different methods in order to calculate themost accurate MetHb reading.

FIG. 4 is a block diagram of a physiological parameter system havingsignal normalization capability.

FIG. 5 illustrates an embodiment of a method for normalizing a signalacquired by a sensor.

FIG. 6 is a general block diagram of a physiological parameter systemhaving alarm, diagnostic and control outputs.

FIG. 6A illustrates an embodiment of a physiological parameter system600 similar to the system in FIG. 6

FIG. 7 is a block diagram of a physiological parameter system combiningpulse oximetry and capnography and providing alarm outputs.

FIG. 8 is a block diagram of a saturation limit alarm enhanced by ETCO₂measurements.

FIG. 9 is a block diagram of a CO₂ waveform alarm enhanced by SpO₂measurements.

FIG. 10 is a block diagram of a physiological parameter system combiningpulse oximetry and capnography and providing a diagnostic output.

FIGS. 11A-12 are block diagrams of a physiological parameter systemutilizing pulse oximetry to control patient controlled analgesia (PCA).

FIGS. 13-13B illustrates an embodiment of a system that displays anindicator of the wellness of a patient.

FIG. 14 is a flowchart showing an example method of displaying anindicator of the wellness of a patient.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Hereinafter, various example embodiments of the present disclosure willbe described in detail with reference to the attached drawings such thatthe present disclosure can be put into practice by those skilled in theart. However, the present disclosure is not limited to the exampleembodiments, but may be embodied in various forms.

Some embodiments will be described in the context of computer-executableinstructions, such as program modules, being executed by hardwaredevices, such as embedded processors, microcontrollers, and computerworkstations. Program modules may include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular data types. Computer-executable instructions,associated data structures, and program modules represent examples ofprogram code for executing steps of the methods disclosed herein. Theparticular sequence of executable instructions or arrangement ofassociated data structures represents examples of corresponding acts forimplementing the functions described in such steps. A person of skill inthe art would understand that other structures, arrangements, andexecutable instructions could be used with the present disclosurewithout departing from the spirit thereof.

FIG. 1 illustrates an embodiment of a physiological measurement system100 having a monitor 101 and a sensor assembly 101. The physiologicalmeasurement system 100 allows the monitoring of a person, including apatient. In particular, the multiple wavelength sensor assembly 101allows the measurement of blood constituents and related parameters,including oxygen saturation, COHb, MetHb and pulse rate.

In an embodiment, the sensor assembly 101 is configured to plug into amonitor sensor port 103. Monitor keys 105 provide control over operatingmodes and alarms, to name a few. A display 107 provides readouts ofmeasured parameters, such as oxygen saturation, pulse rate, COHb andMetHb to name a few.

FIG. 2A illustrates a multiple wavelength sensor assembly 201 having asensor 203 adapted to attach to a tissue site, a sensor cable 205 and amonitor connector 201. In an embodiment, the sensor 203 is incorporatedinto a reusable finger clip adapted to removably attach to, and transmitlight through, a fingertip. The sensor cable 205 and monitor connector201 are integral to the sensor 203, as shown. In alternativeembodiments, the sensor 203 can be configured separately from the cable205 and connector 201, although such communication can advantageously bewireless, over public or private networks or computing systems ordevices, through intermediate medical or other devices, combinations ofthe same, or the like.

FIGS. 2B-C illustrate alternative sensor embodiments, including a sensor211 (FIG. 2B) partially disposable and partially reusable (resposable)and utilizing an adhesive attachment mechanism. Also shown is a sensor213 being disposable and utilizing an adhesive attachment mechanism. Inother embodiments, a sensor can be configured to attach to varioustissue sites other than a finger, such as a foot or an ear. Also asensor can be configured as a reflectance or transflectance device thatattaches to a forehead or other tissue surface. The artisan willrecognize from the disclosure herein that the sensor can includemechanical structures, adhesive or other tape structures, Velcro wrapsor combination structures specialized for the type of patient, type ofmonitoring, type of monitor, or the like.

Certain physiological parameters and certain changes in physiologicalparameters may serve as indicators of an adverse condition affecting apatient. For example, an increase in blood methemoglobin (MetHb)concentration may be useful as a marker of the onset of sepsis or septicshock. As another example, measurements of high blood carboxyhemoglobin(COHb) concentration may indicate exposure to carbon monoxide (CO).Other physiological and related parameters to which techniques of thepresent disclosure may be applicable include respiration rate,respiration volume, oxygen saturation, pulse rate, ECG, blood glucose,blood pressure, temperature, perfusion index, exhaled carbon dioxidewaveform, end tidal carbon dioxide, various signal quality indicators,data confidence indicators and trend data, among others.

A sensor measuring a physiological parameter (e.g., a physiologicalparameter measurement device) of a patient may, under certaincircumstances, detect a change in the magnitude of a detected signalthat does not correspond to a change in the value of the physiologicalparameter. Such changes in a detected signal may occur, for example,when the sensor is moved to a different measurement site. Sometimes, asensor may be temporarily removed from a patient, and medical reasonsmay compel movement of the sensor to a different location. For example,a multiple wavelength sensor may need to be moved to a different fingerof a patient about every 12 hours in order to maintain the sensor'smeasurement effectiveness and/or to avoid injury to the patient. Whenthe measurement site of a multiple wavelength sensor is switched to adifferent location, the magnitudes of some of the signals detected bythe sensor may change, even though no significant change in thepatient's physiological parameters has occurred during the brief sensorrelocation period. Signal normalization techniques described in thepresent disclosure may reduce changes in physiological parametersreported by a physiological parameter system that are unrelated toactual physiological parameter variation.

In some cases, the magnitude of a sensor measurement may be a lesseffective indicator of an adverse condition than a change in themagnitude of a sensor measurement. In such cases, a sensor may not needto be calibrated to report the absolute magnitude of a physiologicalparameter when changes in the magnitude of the parameter are moresignificant for purposes of condition detection. In other cases, theabsolute magnitude of a physiological parameter is valuable, and asensor signal must be analyzed and/or recalibrated to compensate forchanges in the magnitude of the signal detected that do not correspondto changes in the value of the physiological parameter being measured.Signal normalization techniques may improve a physiological parametersystem's reporting effectiveness for both types of parameters.

FIG. 3A illustrates an example chart 300 of the value of a physiologicalparameter, such as, for example, MetHb, as measured by a sensor during atime when the sensor is moved from one measurement site to another.Chart 300 shows the magnitude of a signal measured by a sensor as afunction of time before any analysis or manipulation of the signaloccurs. A first axis 302 of chart 300 represents time, and a second axis304 represents the magnitude of a signal, corresponding to aphysiological parameter, detected at a point in time. The physiologicalparameter corresponding to the signal shown by way of example in FIG. 3Ais blood MetHb concentration.

Curve 306 represents the magnitude of the signal detected by a sensorduring a period when the sensor was at a first measurement site. Thesignal represented by curve 306 roughly oscillates about a nearlyconstant mean value of the signal. However, the signal may also followany continuous increasing or decreasing trend and may also benonoscillatory or contain a complex pattern of variation.

At time T1 along axis 302, the sensor is removed from the firstmeasurement site. Curve 308 represents the magnitude of the signaldetected by the sensor while it is disconnected from the patient, forexample, while a care provider switches the sensor to a new measurementsite. In chart 300, the magnitude of the signal is about zero, but thesensor may continue to detect a signal of some nature (e.g., randomnoise, background interference, etc.) during a period when it isdisconnected from a patient.

At time T2 along axis 302, the sensor is attached to a secondmeasurement site on the patient. The second measurement site may bedifferent than the first measurement site; for example, the secondmeasurement site may be a different finger or a different position on afinger. Curve 310 represents the magnitude of the signal detected by thesensor during a period when the sensor is at the second measurementsite. The signal represented by curve 310 roughly oscillates about anearly constant mean value of the signal that is higher than the meanvalue of the portion of the signal represented by curve 306. Thedifference between the magnitude of the signal shortly before time T1and the magnitude of the signal shortly after time T2 is a shift in themagnitude of the signal that is related to the change in the measurementsite. However, the shift in the signal may not correspond to an actualchange in the value of a physiological parameter of the patient. In somecases, it may be safe to assume that the approximate value of aphysiological parameter shortly before time T1 and shortly after time T2is the same. In the absence of signal normalization, the signal shiftmay trigger a false alarm or cause a physiological parameter system toincorrectly report a change in a parameter. In the embodiment shown inFIG. 3A, reporting the non-normalized signal may trigger an alarm forsepsis or septic shock at time T2 due to an apparent increase in bloodMetHb concentration.

FIG. 3B illustrates a chart 350 of a physiological parameter reported bya measurement system employing signal normalization techniques. In thesituation corresponding to chart 350, it is assumed that the approximatevalue of the physiological parameter shortly before time T1 is the sameas the approximate value of the physiological parameter shortly aftertime T2. A first axis 352 of chart 350 represents time, and a secondaxis 354 represents the value of a physiological parameter reported by aphysiological parameter system at a point in time. The physiologicalparameter shown by way of example in FIG. 3B is blood MetHbconcentration.

In chart 350, curve 356 represents the value of the physiologicalparameter reported while the sensor is at the first measurement site.Curve 358 represents the value of the physiological parameter reportedwhile the sensor is not connected to the patient. In alternativeembodiments, a physiological parameter system may not report a parameteror may shut off the sensor when the system detects that the sensor isnot at a measurement site. Curve 360 represents the value of thephysiological parameter reported while the sensor is at the secondmeasurement site. The physiological parameter data in chart 350 isnormalized because the value of the physiological parameter reportedjust before T1 is adjusted to match the value of the physiologicalparameter just after T2. Various methods of matching may exist,including adjusting the values before and after the measurement sitechange to be approximately equal, using data points before T1 togenerate a trend line and fixing the data point at T2 to the trend line,or any other method known in the art of projecting or approximating thevalue of the physiological parameter at T2 based on data prior to T1.

In some embodiments, sensor measurements that are received after timeT2, as shown in curve 310 of chart 300 (FIG. 3A), may be normalized byadding an offset to the magnitudes of the measurements. The offset maybe calculated by subtracting the magnitude of the non-normalized sensormeasurement at time T2 from the magnitude of the normalized sensormeasurement at T2. The offset may be a negative number. Similar methodsof normalizing data points involving, for example, subtraction of anoffset and other known methods may also be used. One result of signalnormalization is that, given a relatively constant physiologicalparameter over time, the mean value of curve 360 will more closelyapproximate the mean value of curve 356. Signal normalization may reducethe incidence of false alarms and reports of changes in physiologicalparameters that have not in fact changed.

FIG. 3C illustrates a further example of normalizing a signal witherratic noise, such as, for example, motion induced noise. Asillustrated, a physiological parameter signal 370, such as a signalindicative of MetHb, is illustrated. The physiological parameter signal370 includes various inconsistencies, such as, for example, erraticnoises 371, probe off conditions 373, and cite change conditions 375. Inorder to deal with these inconsistencies, processing is used todetermine a normalization 377 or trend of the signal. The normalization377 uses various methods in order to determine a relatively stablephysiological parameter reading 377.

FIG. 3D illustrates a flow chart of a normalization procedure 380. Forease in discussion, FIGS. 3D and 3E will be discussed with respect to aMetHb reading, however, it should be understood that any physiologicalparameter can be used with the present disclosure. The normalizationprocedure begins with the data signal 381. As show, the normalizationfeature 380 includes Met calculator 382; smoother 384, Met signalextractor 385; signal quality 387 and distortion 388. In an embodiment,a data signal 381 responsive to an intensity signal is input into theMet calculator 382, and a current value 383 of Met is calculated. Thecurrent value 383 of Met, which in an embodiment is subject to noise,distortion, and site movements in the data signal 381, is input into thesmoother 384, which reduces an error between the current value 383 ofMet and actual MetHb conditions. For example, the smoother 384 mayadvantageously determine a Met trend, and depending upon an indicationof some or all of an amount of distortion, noise, signal quality, and/orwaveform quality in the data signal 383, substitute or combine the MetHbtrend for or with the current value 383 to generate an output MetHbmeasurement.

In an embodiment, the distortion signal 388 may comprise a Boolean valueindicating whether the data signal 383 includes, for example,motion-induced noise. Although an artisan will recognize from thedisclosure herein a number of methodologies for deriving the distortionsignal 388, derivation of a Boolean distortion signal is disclosed inU.S. Pat. No. 6,606,511, incorporated herein by reference.Alternatively, or in addition to, the signal quality signal 387 maycomprise a Boolean value indicating whether the data signal 383 meetsvarious waveform criteria Although an artisan will recognize from thedisclosure herein a number of methodologies for deriving the signalquality signal 387, derivation of a Boolean distortion signal isdisclosed in the '511 patent. Alternatively, or in addition to, afeature extractor 385 may advantageously produce waveform qualityoutputs 386, indicative of waveform quality or waveform shape. Althoughan artisan will recognize from the disclosure herein a number ofmethodologies for deriving the waveform quality signal 386, derivationthereof is disclosed in U.S. Pat. No. 6,334,065, also incorporatedherein by reference.

Thus, the smoother 384 accepts one or more or different indicators ofthe quality of the data signal 381, and determines how to smooth ornormalize the output to reduce errors between data trends and actualMetHb conditions. In an embodiment, the smoothing may advantageouslycomprise statistical weighting, other statistical combinations, orsimply passing the MetHb measurement 383 through to the output,depending upon one or more of the quality signals 386, 387, 388, orlogical combinations thereof.

Upon the output of the normalized MetHb measurement, a monitor mayadvantageously audibly and/or visually presents the measurement to acaregiver, and when the measurement meets certain defined thresholds orbehaviors, the monitor may advantageously audibly and/or visually alertthe caregiver. In other embodiments, the monitor may communicate withother computing devices to alert the caregiver, may compare longer termtrend data before alarming, or the like.

FIG. 3E illustrates a simplified block diagram of an embodiment of aMetHb determination system 390 using multiple Met calculationtechniques. As shown, data 391 is input into the system. The data 391 isthen routed to at least two different Met calculators 392, 393. In anembodiment, more than two different types of calculation techniques canbe used. The at least two Met calculators 392, 393 output Metindications for input into the Met selector 395. The Met selector 395determines a Met value to output. The Met selector chooses the outputbased on which Met calculator works best for a given condition of thesignal or based on which Met calculation fits the trend of Met readings.Other methods of selecting the best Met value can also be made as wouldbe understood by a person of skill in the art from the presentdisclosure.

FIG. 4 is a block diagram of a physiological parameter system havingsignal normalization capability. A physiological parameter system mayinclude a sensor signal analysis subsystem 400 that implements signalnormalization techniques. Signal analysis subsystem 400 receives asignal 402 from a physiological parameter measurement device output.Signal 402 may be, for example, an electrical signal produced by anoptical transducer within a pulse oximeter or a capnometer.

In the embodiment shown in FIG. 4, signal 402 is communicated to asensor event module 404. Sensor event module 404 includes program codefor detecting events that occur based on a pattern recognized in signal402. Detected events may include a change in measurement site, movementof the sensor, interference in the signal, etc. For example, sensorevent module 404 may determine that a measurement site of the sensor hasbeen exchanged if a normal physiological parameter pattern ceases for ashort period of time and then resumes. Alternatively, sensor eventmodule 404 may detect a measurement site switch when signal 402 isinterrupted by an interval of random noise and/or a relatively largediscontinuity in the signal. Alternatively, an operator can indicate anevent, such as a location change, by, for example, pressing apredetermined function button. As another example, sensor event module404 may determine that signal normalization may not be appropriate whena sensor has been disconnected from a measurement site for asufficiently long period of time (e.g., when an assumption that a signaltrend will continue is no longer sound). Sensor event module 404 maycommunicate signal 402 and/or event information to a sensor memory 406to store sensor signal pattern data for later use. Sensor event module404 may also communicate signal 402 and event information to signalnormalization module 408.

Sensor memory 406 may retain a certain number of signal 402 samples ormay retain signal 402 samples for a certain period. Retained samples maybe used by program code in signal normalization module 408 and/or sensorevent module 404. Samples from signal 402 may be stored in a queue datastructure, for example. In some embodiments, sensor event module 404 mayinstruct sensory memory 406 to cease storing new samples when itdetermines that the sensor is not connected to a measurement site sothat signal data for potential future signal normalization may beretained. Signal memory 406 may also retain signal offset or calibrationdata.

Signal normalization module 408 comprises program code for converting asignal 402 from a sensor output into a normalized measure of aphysiological parameter. Program code in module 408 may, for example,add or subtract a value from signal 402 in order to eliminate shifts inthe magnitude of signal 402 that are not related to variation in apatient's physiological parameters. Signal normalization module 408 maydetermine an offset that counterbalances a shift in signal 402 thatresults from a change in sensor measurement site. Module 408 may includeprogram code for calculating a trend line from data stored in sensormemory 406. A trend line may be used to determine an appropriate valuefor a patient parameter when measurement resumes after an interruptionin signal 402. Module 408 may also employ pattern recognition or signaltransforms to help it determine how signal 402 should be normalized.Sensor event module 404 may trigger signal normalization module 408 toreset its signal normalization when a certain signal events aredetected. In some embodiments, sensor event module 404 may communicateto signal normalization module 408 the retained signal data from sensormemory 406 it should use to calculate a new offset. Signal normalizationmodule 408 passes a normalized signal 450 out of signal normalizationsubsystem 400.

Normalized signal 450 may then be passed to other components of aphysiological parameter system for further analysis and/or display. Forexample, normalized signal 450 may be communicated to a comparator 454that compares signal 450 to one or more parameter limits 452. In someembodiments, comparator 454 may generate an alarm signal 456 ifnormalized signal 450 falls outside of parameter limits 452.

FIG. 5 illustrates an embodiment of a method for normalizing a signalacquired by a sensor when the measurement site of the sensor is changed.At step 502, sensor memory 406 (FIG. 4) maintains recent physiologicalparameter measurements received from sensor output 402. Sensor signaldata may be passed directly to sensor memory 406 for storage, or sensorevent module 404, for example, may select which signal samples will beretained and pass them to sensor memory 406. Retained signal sample datamay include the magnitude of the signal as well as an indicator of thetime that the sample was taken and/or the order in which the sample wasreceived. Alternatively, sensor memory 406 may simply maintain signaldata in chronological order in a queue, purging old sample data as newsample data is received. Data may be retained only for a certain timeinterval, such several seconds, a fraction of a minute, a minute, twominutes, or longer. The interval of retention may vary depending on thephysiological parameter associated with signal 402. This step maycontinue until sensor event module 404 detects a sensor measurement sitechange.

In step 504 of FIG. 5, sensor event module 404 detects a change in thesensor measurement site. In some embodiments, sensor event module 404may detect the change in measurement site by one of the methodsdescribed with respect to the description of program code within sensorevent module 404 above. Alternatively, a user of a physiologicalparameter system may indicate that a change in sensor measurement sitehas occurred by means of a hardware or software interface. For example,the sensor may include a hardware switch that activates when themeasurement site is changed. The system may also include a manual switchor button that a user can activate to cause sensor event module 404 toregister a change in the sensor measurement site. When sensor eventmodule 404 determines that sampling at the new measurement site hasbegun, the method proceeds to step 506.

At step 506, signal normalization module 408 compares the magnitude ofthe signal sampled at the new measurement site with the magnitude of theretained signal that was obtained at the old measurement site. Signalnormalization module 408 may use pattern recognition or signal transformtechniques to attempt to compare an oscillatory signal at similar pointsin its cycle to obtain a more accurate comparison. In some embodiments,module 408 uses the comparison to calculate an offset that adjusts thesignal at the time that measurement at the new measurement site beginsto conform to a trend line fitted to signal data acquired from the oldmeasurement site. Retained signal data from the old measurement site maybe retrieved from sensor memory 406 and analyzed for the purpose ofcalibrating the sensor signal at the new measurement site. After theinitial physiological parameter value is projected when the sensorbegins sampling at the new measurement site, the method proceeds to step508.

In step 508, signal normalization module 408 adjusts the magnitude ofthe signal measured at the new measurement site in order to output anormalized signal 450. In some embodiments, adjusting the magnitude ofthe signal measured comprises modifying the magnitude of a signalmeasure measurement by adding or subtracting an offset. For example, theoffset may be calculated by subtracting the magnitude of the signalsampled just after the sensor begins measurements at the new measurementsite from the magnitude of the signal sampled just before the sensor wasremoved from the old measurement site. Alternatively, the offset may bedefined as the difference between (1) a projected value of the magnitudeof the signal just after the sensor begins measurements at the newmeasurement site, the projection based on measurements at the oldmeasurement site, and (2) the actual measured value of the magnitude ofthe signal just after the sensor begins measurements at the newmeasurement site. Any other known means for calculating an offset mayalso be used. Signal normalization module 408 continues to add orsubtract the calculated offset until another normalization step isrequired. At the conclusion of the method shown in FIG. 5, the stepsshown may be repeated as many times as changes in the measurement siteof the sensor may require.

Various embodiments of signal normalization techniques have been shownand described. Some alternative embodiments and combinations ofembodiments disclosed herein have already been mentioned. Additionalembodiments comprise various other combinations or alterations of theembodiments described.

FIG. 6 illustrates a physiological parameter system 600, which maycomprise an expert system, a neural-network or a logic circuit, forexample. The physiological parameter system 600 has as inputs 601 fromone or more parameters from one or more physiological measurementdevices, such as a pulse oximeter 610 and/or a capnometer 620. Pulseoximeter parameters may include oxygen saturation (SpO₂), perfusionindex (PI), pulse rate (PR), various signal quality and/or dataconfidence indicators (Qn) and trend data, to name a few. Capnographyparameter inputs may include, for example, an exhaled carbon dioxidewaveform, end tidal carbon dioxide (ETCO₂) and respiration rate (RR).Signal quality and data confidence indicators are described in U.S. Pat.No. 6,108,090 cited above. The physiological parameter system 600 mayalso have parameter limits 606, which may be user inputs, defaultconditions or otherwise predetermined thresholds within the system 600.

The inputs 601 are processed in combination to generate one or moreoutputs 602 comprising alarms, diagnostics and controls. Alarms may beused to alert medical personnel to a deteriorating condition in apatient under their care. Diagnostics may be used to assist medicalpersonnel in determining a patient condition. Controls may be used toaffect the operation of a medical-related device. Other measurementparameters 630 that can be input to the monitor may include or relate toone or more of ECG, blood glucose, blood pressure (BP), temperature (T),HbCO, MetHb, respiration rate and respiration volume, to name a few.

FIG. 6A illustrates an embodiment of a physiological parameter system600 similar to the system in FIG. 6. The physiological parameter system600 has as inputs 601 from one or more parameters from one or morephysiological measurement devices, such as, for example a pulse oximeter610, an acoustic respiratory monitor 640, an ECG monitor 650, aninvasive or non-invasive blood pressure monitor 650, a thermometer, orany other invasive or noninvase physiological monitoring devices or thelike.

FIG. 7 illustrates one embodiment of a physiological parameter system700 combining pulse oximetry parameter inputs 710 and capnographyparameter inputs 720 so as to generate alarm outputs 702. Parameterlimits 705 may be user inputs, default conditions or otherwisepredetermined alarm thresholds for these parameters 710, 720. The alarms702 are grouped as pulse oximetry related 730, capnography related 740and a combination 750. For example, a pulse oximetry alarm 730 may berelated to percent oxygen saturation and trigger when oxygen saturationfalls below a predetermined percentage limit. A capnography alarm 740may be related to ETCO₂ and trigger when ETCO₂ falls below or risesabove a predetermined mm Hg pressure limit. A combination alarm 750 mayindicate a particular medical condition related to both pulse oximetryand capnography or may indicate a malfunction in either instrument.

FIG. 8 illustrates a SpO₂ alarm embodiment 800 that is responsive toETCO₂. In particular, a SpO₂ alarm 805 may be triggered sooner and mayindicate a high priority if ETCO₂ 803 is falling. That is, if ETCO₂ 803is trending down above a certain rate, the SpO₂ alarm 805 is triggeredat a higher percentage oxygen saturation threshold and alerts acaregiver to the possibility of a serious condition, e.g. a pulmonaryembolism.

As shown in FIG. 8, a slope detector 810 determines the slope 812 of theETCO₂ input 803. A slope comparator 820 compares this slope 812 to apredetermined slope limit 804. If the downward trend of ETCO₂ 803 isgreat enough, a delta value 803 is added 840 to the SpO₂ lower limit 802to generate a variable threshold 842. A threshold comparator 850compares this variable threshold 842 to the SpO₂ input 801 to generate atrigger 852 for the SpO₂ alarm 805. The alarm volume, modulation or tonemay be altered to indicate priority, based upon the slope comparatoroutput 822.

FIG. 9 illustrates a CO₂ alarm embodiment 900 that is responsive toSpO₂. In particular, morphology of the input CO₂ waveform 901 isutilized to trigger an alarm 905, and that alarm is also responsive to afalling SpO₂ 902. That is, if a pattern in the CO₂ waveform is detectedand SpO₂ is trending down above a certain rate, then an alarm istriggered. For example, an increasing slope of the CO₂ plateau incombination with a downward trend of SpO₂ may trigger an alarm and alerta caregiver to the possibility of an airway obstruction.

As shown in FIG. 9, a pattern extractor 910 identifies salient featuresin the CO₂ waveform and generates a corresponding feature output 912. Apattern memory 920 stores one or more sets of predetermined waveformfeatures to detect in the CO₂ input 901. The pattern memory 920 isaccessed to provide a feature template 922. A feature comparator 930compares the feature output 912 with the feature template 922 andgenerates a match output 932 indicating that a specific shape or patternhas been detected in the CO₂ waveform 901. In addition, a slope detector940 determines the slope 942 of the SpO₂ input 902. A slope comparator950 compares this slope 942 to a predetermined slope limit 904. If thedownward trend of SpO₂ 902 is great enough, a slope exceeded output 952is generated. If both the match output 932 and the slope exceeded output952 are each asserted or “true,” then a logical AND 960 generates atrigger output 96 to the alarm 970, which generates an alarm output 905.

FIG. 10 illustrates a combination embodiment 1000 having a diagnosticoutput 1005 responsive to both SpO₂ 1001 and CO₂ 1003 inputs. A SpO₂slope detector 100 determines the slope 102 of the SpO₂ input 1001 andcan be made responsive to a negative slope, a positive slope or a slopeabsolute value. A first comparator 1020 compares this slope 102 to apredetermined SpO₂ slope limit 1002. If the trend of SpO₂ 1001 is greatenough, a SpO₂ slope exceeded output 1022 is asserted. Likewise, an CO₂slope detector 1030 determines the slope 1032 of the CO₂ input 1003. Asecond comparator 1040 compares this slope 1032 to a predetermined CO₂slope limit 1004. If the downward trend of CO₂ 1001 is great enough, anCO₂ slope exceeded output 1042 is asserted. If both slope exceededoutputs 1022, 1042 are asserted or “true,” a diagnostic output 1005 isasserted.

In one embodiment, the slope detectors 610, 1030 are responsive to anegative trend in the SpO₂ 1001 and CO₂ 1003 inputs, respectively.Accordingly, the diagnostic output 1005 indicates a potential embolismor cardiac arrest. In another embodiment, the SpO₂ slope detector 610 isresponsive to negative trends in the SpO₂ 1001 input, and the CO₂ slopedetector 1030 is responsive to a positive trend in the CO₂ 1003 input.Accordingly, the diagnostic output 1005 indicates a potential airwayobstruction. The diagnostic output 1005 can trigger an alarm, initiate adisplay, or signal a nursing station, to name a few.

FIGS. 11A-B illustrate a physiological parameter system 1100 utilizingpulse oximetry to control patient controlled analgesia (PCA). Inparticular embodiments, a control output 1108 is responsive to pulseoximetry parameters 1101 only if signal quality 1103 is above apredetermined threshold 1104. In FIG. 11A, the control output 1108 canbe used to lock-out patient controlled analgesia (PCA) if pulse oximetryparameter limits have been exceeded. If signal quality is so low thatthose parameters are unreliable, however, PCA is advantageously allowed.That is, the pulse oximeter parameters are not allowed to lock-out PCAif those parameters are unreliable. By contrast, in FIG. 11B, thecontrol output 1108 can be used to advantageously lock-out or disablepatient controlled analgesia (PCA) if pulse oximetry parameter limitshave been exceeded or if signal quality is so low that those parametersare unreliable.

As shown in FIG. 11A, pulse oximetry parameters 1101 and correspondinglimits 1102 for those parameters are one set of inputs and a signalquality measure 1103 and a corresponding lower limit 1104 for signalquality are another set of inputs. The parameters 1101 and correspondinglimits 1102 generate a combined output 1202 that is asserted if any ofthe pulse oximetry parameter limits are exceeded. A comparator 1110compares the signal quality 1103 input with a lower limit 1104generating a quality output 1112 that is asserted if the signal quality1103 drops below that limit 1104. An AND logic 1120 generates a reset1122 if the combined output 1202 is asserted and the quality output 1112is not asserted. The reset 1122 resets the timer 1130 to zero. Acomparator 1140 compares the timer output 1132 to a predetermined timelimit 1106 and generates a trigger 1142 if the time limit is exceeded.The trigger 1142 causes the control 1150 to generate the control output1108, enabling a patient controlled analgesia (PCA), for example. Inthis manner, the PCA is enabled if all monitored parameters are withinset limits and signal quality is above its lower limit for apredetermined period of time.

As shown in FIG. 11B, the combined output 1202, quality output 1112,reset 1122, timer 1130, comparator 1140 and control 1150 are generatedas described with respect to FIG. 11A, above. An OR logic 1121 generatesa reset 1122 if either the combined output 1202 or the quality output1112 is asserted. In this manner, the PCA is disabled for apredetermined period of time if any of the monitored parameters areoutside of set limits or the signal quality is below its lower limit.

FIG. 12 illustrates combined limits 1200 having SpO₂ parameters 1101 andcorresponding thresholds 1102 as inputs and providing a combinationoutput 1202. In particular, if any parameter 1101 exceeds itscorresponding limit 1102, the output of the corresponding comparator1210, 1220, 1240 is asserted. An OR logic 1250 is responsive to anyasserted output 1212, 1222, 1242 to asserted the combined output 1202.For example, the combined output 1202 may be asserted if SpO₂ 1201 fallsbelow a lower limit 1209, pulse rate (PR) 1203 rises above an upperlimit 1204 or PR 1203 falls below a lower limit 120.

A physiological parameter system has been disclosed in detail inconnection with various embodiments. These embodiments are disclosed byway of examples only and are not to limit the scope of the claims thatfollow. One of ordinary skill in the art will appreciate many variationsand modifications. For example, the control output 1108 (FIG. 11B) canbe used to control (titrate) delivered, inspired oxygen levels topatients based upon pulse oximetry parameters, unless signal quality isso low that those parameters are unreliable. One of ordinary skill inthe art will also recognize that the control output 1108 (FIG. 11B) canbe used to control patient delivery of any of various pharmacologicalagents and/or medical gases.

FIG. 13 illustrates an embodiment of a system 1300 that displays anindicator of the wellness of a patient. Various sensors 1302 a-1302 ncommunicate with a parameter analysis module 1306. Sensors 1302 a-1302 nmay include pulse oximeters and capnometers, among other physiologicalparameter measurement devices. Sensor 1302 n outputs a signal that maybe sampled, normalized, and/or analyzed by modules that are not shown insystem 1300 before being passed to parameter analysis module 1306. Asdescribed above, normalization of sensor signals before comparison ofthe signals to parameter limits 452 (FIG. 4) and/or parameterpreferences 1304 may have certain benefits, such as decreased incidenceof false alarms and/or more effective determination of the wellness ofthe patient.

In the embodiment shown, a user may provide parameter preferences 1304to parameter analysis module 1306 through a user interface. Parameterpreferences 1304 may include preferred ranges, less preferred ranges,least preferred ranges, upper limits, lower limits, preferred rates ofincrease or decrease, preferred patterns or trends, preferred states, orany combination of such preferences or other standards for evaluatingthe desirability of various physiological parameter values and signals.In some cases, a user of system 1300 may provide custom preferences tooverride a default set of physiological parameter preferences 1304preprogrammed into system 1300. In some embodiments, parameter analysismodule 1306 may include program code for dynamically changing orsuggesting changes to various parameter preferences as a function ofcertain physiological parameters or related sensor performance data.

Parameter analysis module 1306 compares at least some of the signal datareceived from sensors 1302 a-1302 n to parameter preferences 1304 inorder to calculate an indicator of the wellness of a patient. In someembodiments, the indicator calculated is a numerical indicator; forexample, a number between one and ten, where a ten corresponds to apatient with a high level of wellness, and a one corresponds to apatient with a very low level of wellness as depicted in FIG. 13B. Otherranges, such as one to 100, −100 to 100, etc., and scales, such as analphabetic A-F scale or a color scale, may also be used including thescale depicted in FIG. 13A. Other indicators that may be generated byparameter analysis module 1306 include graphical indicators of potentialtrouble areas, gauges, charts, level meters, and the like may also beused. Parameter analysis module 1306 communicates the indicator to adisplay 1308, which may display the indicator in any suitable graphicalor textual form that is known in the art. For example, display 1308 mayshow a number of bars or a level meter, the number of which maycorrespond to one of the numerical indicator scales discussed above.

FIG. 14 is a flowchart showing an example method of displaying anindicator of the wellness of a patient. At step 1402, parameter analysismodule 1306 (FIG. 13) receives signal data from one or more sensors 1302a-1302 n. As discussed previously, such signal data may be normalized orotherwise modified from its raw form before being passed to parameteranalysis module 1306. Parameter analysis module 1306 may continuouslyupdate an indicator as new data is received and may calculate averages,variances, and/or other analytical measures of various physiologicalparameters over time. In some embodiments, parameter analysis module1306 may update the indicator of patient wellness only periodically,sporadically, or by request rather than continuously, thus requiringonly occasional reception of data from sensors 1302 a-1302 n.

In step 1404, parameter analysis module 1306 receives parameterpreferences 1304. Preferences 1304 may by received only once orsporadically as a user supplies custom preferences. Preferences 1304 mayalso be received and/or updated continuously when, for example,parameter preferences 1304 are functions of various physiological orsampling parameters.

At step 1406, parameter analysis module 1306 compares the data receivedfrom sensors 1302 a-1302 n to parameter preferences 1304. Individualsensor measurements may be compared to parameter preferences 1304, orparameter analysis module may compare parameter preferences 1304 to amoving average of sensor measurements, for example. Comparison ofvarious other known analytical measures of sensor data is also possibleand within the scope of the present disclosure. The comparison performedby parameter analysis module 1306 may include magnitude comparisons,pattern analysis, and/or trend analysis. Historical sensor data may alsobe used in the comparison.

In step 1408 of FIG. 14, parameter analysis module 1306 generates anindicator of the wellness of the patient based on the comparisonperformed in step 1406. The indicator may be in any of the formsdiscussed previously. For example, module 1306 may increase a wellnessscore (e.g., a numerical indicator of wellness) when physiologicalparameters fall within preferred ranges or when sensor signals followpreferred patterns and/or trends. The indicator may comprise a simple ora more detailed textual and/or graphical summary of the patient'swellness as interpreted from parameters measured by sensors 1302 a-1302n. In some embodiments, the indicator may be a scaled number incombination with a textual description of the patient's wellness scoreand/or conditions that may be affecting the score. In addition,particular a particular condition affecting the patient can also begenerated for communication to a healthcare provider, such as, forexample, sepsis, septic shock, apnea, heart failure, airway obstruction,carbon monoxide poisoning, low oxygen content, etc.

After parameter analysis module 1306 generates the wellness indicator,it sends the indicator to display 1308 at step 1410. Display 1308 may beintegrated with physiological parameter system 1300 or may be a separatedisplay device. The display may also include auditory sounds, such asfor example, beeps, voices, words, etc., to indicate a particular eventor condition occurring.

Although the foregoing invention has been described in terms of certainpreferred embodiments, other embodiments will be apparent to those ofordinary skill in the art from the disclosure herein. Additionally,other combinations, omissions, substitutions and modifications will beapparent to the skilled artisan in view of the disclosure herein. It iscontemplated that various aspects and features of the inventiondescribed can be practiced separately, combined together, or substitutedfor one another, and that a variety of combination and subcombinationsof the features and aspects can be made and still fall within the scopeof the invention. Furthermore, the systems described above need notinclude all of the modules and functions described in the preferredembodiments. Accordingly, the present invention is not intended to belimited by the recitation of the preferred embodiments, but is to bedefined by reference to the appended claims.

1. A method of detecting a physiological condition from trend datathrough a change in measurement site, the method comprising: obtaining ameasurement of a physiological parameter from a first measurement siteof a patient; obtaining a measurement of the physiological parameterfrom a second measurement site of the patient; determining trendinformation of the physiological parameter from measurements at thefirst and second sites; and determining a physiological condition basedon the trend information.
 2. The method of claim 1, wherein thephysiological parameter comprises HbMet.
 3. The method of claim 1,wherein the physiological condition comprises sepsis.
 4. The method ofclaim 1, wherein the physiological condition comprises septic shock. 5.A method of measuring a physiological parameter in a physiologicalparameter system comprising: obtaining measurements of a physiologicalparameter from a first measurement site of a patient; determining achange in the measurement site of the patient; obtaining a measurementof the physiological parameter from a second measurement site of thepatient; comparing the measurement of the physiological parameter at thesecond measurement site with the measurement from the first measurementsite; and calibrating the physiological parameter measurements at thesecond site with the physiological measurements at the first site. 6.The method of claim 5, wherein calibrating comprises adding an offset tothe measurement of the physiological parameter at the second measurementsite.
 7. The method of claim 6, wherein the offset is calculated bysubtracting the measurement of the physiological parameter at the secondmeasurement site with the most recent physiological parametermeasurement from the first site.
 8. The method of claim 6, wherein theoffset is calculated by subtracting the measurement of the physiologicalparameter at the second measurement site with a projection of thephysiological parameter at the second measurement site extrapolated froma trend line fit to the physiological parameter measurements of thefirst site.
 9. A system for calibrating physiological measurements fromtwo different sites comprising: a sensor configured to obtain anindication of a physiological condition at a measurement site and outputsaid indication; a processor configured to receive said indication anddetermine when a change in measurement sites from a first measurementsite to a second measurement site occurs; wherein the processor isfurther configured to adjust a measurement at the second site based on ameasurement at the first site.
 10. The system of claim 9, wherein thesensor comprises a MetHb sensor.
 11. The system of claim 9, wherein thesensor comprises one or more of a COHb sensor, an SpO2 sensor, a bloodpressure sensor, a respiratory sensor, and an ECG sensor.
 12. A systemfor indicating a wellness state of a patient comprising: a first sensorconfigured to measure a first physiological parameter and output anindication of the first physiological parameter; a second sensorconfigured to measure a second physiological parameter different fromsaid first physiological parameter and output an indication of thesecond physiological parameter; and a processor which receives theoutputs of the first and second sensor, determines an indication ofwellness from the outputs, and outputs an indication of wellness to adisplay device.
 13. The system of claim 12, wherein the first sensor andthe second sensor comprises one or more of a pulse oximetry sensor, ablood pressure sensor, an ECG sensor, an acoustic sensor, or acapnographer.
 14. A method of generating an indicator of patientwellness using a physiological parameter system comprising: receivingphysiological parameter data from a sensor attached to the physiologicalparameter system; providing physiological parameter preferences to thephysiological parameter system; comparing the physiological parameterdata to the physiological parameter preferences; generating an indicatorof patient wellness from the comparison.
 15. The method of claim 14,wherein receiving physiological parameter data comprises receivingphysiological parameter data indicative of a MetHB level.
 16. The methodof claim 14, wherein receiving physiological parameter data comprisesreceiving physiological parameter data indicative of a respiratory rate.17. The method of claim 14, wherein receiving physiological parameterdata comprises receiving physiological parameter data indicative of aCoHb level.
 18. The method of claim 14, wherein receiving physiologicalparameter data comprises receiving physiological parameter dataindicative of a total hemoglobin level.
 19. The method of claim 14,wherein receiving physiological parameter data comprises receivingphysiological parameter data indicative of a respiratory irregularity.20. The method of claim 14, wherein receiving physiological parameterdata comprises receiving physiological parameter data indicative of ablood pressure.
 21. The method of claim 14, wherein receivingphysiological parameter data comprises receiving physiological parameterdata indicative of a glucose level.
 22. The method of claim 14, whereinreceiving physiological parameter data comprises receiving physiologicalparameter data indicative of a SpO2 level.